Over the last couple of years, Google has gradually improved YouTube Music with features like playback screen lyrics and an Explore tab. Now, it has unveiled integration with some of its other products, including Android TV, Google Maps and and Google Assistant. The first feature is recommendations via Google Assistant. To use it, you simply say: "Hey Google, play recommended music from YouTube Music," and you'll get personalized music suggestions, including favorite artists and genres, based on your listening history. Unfortunately, this feature is only available on newer Nest speakers and not Google Home devices.
As IBM (IBM) and Red Hat team up with Adobe (ADBE) on artificial intelligence and personalization technology, Adobe stock is trying to customize a new base and buy point. The IBD Long-Term Leader is also setting its sights on a fresh all-time high. In July, Adobe, IBM and Red Hat announced a strategic partnership aimed at accelerating the digital transformation and strengthening of real-time data security for enterprises, with a focus on regulated industries such as banking and health care. Building on IBM's acquisition of Red Hat in 2018, the goal of the partnership is to "enable companies to deliver more personalized experiences across the customer journey, driving improved engagement, profitability and loyalty." Having already made its own successful shift to a software-as-a-service model, Adobe has become a major player in cloud-based creative, personalization and analytics products.
Alibaba Cloud has added three hyperscale data centres in China and plans to build more over the next few years. The move is part of the Chinese tech giant's $28 billion investment to modernise its cloud infrastructure and support customers' digital transformation needs. Located in Hangzhou, the Jiangsu Province's Nantong, and Inner Mongolia's Ulangab, the three new sites run on Alibaba's own technologies including its Apsara Distributed OS, Hanguang 800 AI chip, and X-dragon architecture. The launch was part of its previous announcement to park another $28 billion over three years to build out its cloud infrastructure, the company said in a statement Tuesday. While coy over how the Huawei-US debacle may impact other Chinese technology vendors, Alibaba Cloud executives play up their "in Asia, for Asia" focus and investment in the region as a key competitive advantage over its US competitors, including AWS, Microsoft, and Google.
We read a lot about IoT, but not clear what exactly it means, although we know about its definition so here we explain in simple terms. IoT is basically connecting of computing devices, mechanical, digital machines, objects, and people with one another. Ex: wirelessly connecting devices such as smart speakers i.e. our very own Amazon Alexa or Google Home, smart TVs, Apple Watch, internet-connected baby monitors, video doorbells, and even toys, CCTV camera's controlled by smartphones. The technology that is concerned with safeguarding the connected devices and networks in the internet of things (IoT). IoT is a concept based on the idea of everyday physical objects with the ability to communicate directly over the Internet.
These days everyone needs their machines to talk, and the only way by which a computer can communicate is through Natural Language Processing (NLP). Take the case of Alexa, a conversational item by Amazon. An inquiry is passed to it by the mode of voice, and it can answer by a similar medium, i.e., voice. The market situation of NLP is quite promising. The buzz of NLP in the market is increasing in an aggressive way which is expected to reach the mark of $ 16 billion by 2021 with the compound growth rate of 16 % yearly.
Improvements in cloud technologies and processing power have provided a solid foundation for mainstream adoption of machine learning (ML). With the ability to analyze massive amounts of data to derive meaningful insights, ML can give business leaders new ways to innovate, create new revenue streams, improve operational efficiencies, and help all employees make faster, more informed decisions. In IDG's 2019 Digital Business Study, 78% of IT and business leaders said their organizations are considering or have already deployed machine learning technologies as part of their digital business strategy. "We've seen it day in and day out with customers we support, and organizations in general, that are benefiting by leveraging machine learning," says Sri Elaprolu, senior leader, Amazon Machine Learning Solutions Lab, a team of data scientists and machine learning experts that helps Amazon Web Services (AWS) customers successfully adopt ML. Amazon is a prime example of how ML can impact every area of the business.
Natural language processing (NLP) technologies are widely deployed to process rich natural language text data for search and recommender systems. Achieving high-quality search and recommendation results requires that information, such as user queries and documents, be processed and understood in an efficient and effective manner. In recent years, the rapid development of deep learning models has been proven successful for improving various NLP tasks, indicating the vast potential for further improving the accuracy of search and recommender systems. Deep learning-based NLP technologies like BERT (Bidirectional Encoder Representations from Transformers) have recently made headlines for showing significant improvements in areas such as semantic understanding when contrasted with prior NLP techniques. However, exploiting the power of BERT in search and recommender systems is a non-trivial task, due to the heavy computation cost of BERT models. In this blog post, we will introduce DeText, a state-of-the-art open source NLP framework for text understanding.
Ever since the introduction of computers, the primary objective of their evolution has been to take arduous calculations off our plates. It meant automating tasks that would otherwise take us a long time. Over the past few years, the computing capabilities of mobile devices have reached a point where it's now easy to deploy machine learning natively. Artificial intelligence is a term that gets thrown around a lot, but it's machine learning that's making automation possible. When we talk about artificial intelligence, we actually refer to its branch called machine learning, which is the way computers learn and perform tasks without being explicitly programmed.
Machine learning (ML) techniques are now widely being used in almost all areas of application. Six months back, CCTech Research started investigating how we may use ML in the area of Design of Mechanical Systems. One of the key application we were particularly interested is in Control Valve industry. Control Valve Performer - a cloud app developed by our simulationHub team is already helping valve manufacturers get quick results. Given a 3D model, our app provides the flow performance parameters such as flow coefficient Cv, Coefficient of Hydrodynamic Torque Cdt in less than an hour.